Remark 1 
In fact, [1]} notes that REF  holds for general distributions. As such, we can conclude from Proposition 1 that \(\mathbf {H(X)^l} \in \mathbb {R}^{h^n_l \times b_s}: b_s \longrightarrow \infty \)  results in small and large distribution ranges, which are admissible for \(\sigma _{min}(\mathbf {H(X)}^l)\)  and \(\sigma _{max}(\mathbf {H(X)}^l)\) , respectively. Accordingly, as \(b_s\)  increases, we have the following scenarios (i) higher probability for \(\mathbf {H(X)}^l\)  to have a small \(\sigma _{min}(\mathbf {H(X)}^l)\) ; and (ii) higher probability for \(\mathbf {H(X)}^l\)  to have a larger condition number.

